Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform

In this paper, we provide details of implementing a system for managing a fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse premise. While the robots are themselves autonomous in its motion and obstacle avoidance capability, the target destination for each robot is provided by a global planner. The global planner and the ground vehicles (robots) constitute a multi agent system (MAS) which communicate with each other over a wireless network. Three different approaches are explored for implementation. The first two approaches make use of the distributed computing based Networked Robotics architecture and communication framework of Robot Operating System (ROS) itself while the third approach uses Rapyuta Cloud Robotics framework for this implementation. The comparative performance of these approaches are analyzed through simulation as well as real world experiment with actual robots. These analyses provide an in-depth understanding of the inner working of the Cloud Robotics Platform in contrast to the usual ROS framework. The insight gained through this exercise will be valuable for students as well as practicing engineers interested in implementing similar systems else where. In the process, we also identify few critical limitations of the current Rapyuta platform and provide suggestions to overcome them.Comment: 14 pages, 15 figures, journal pape

Past and Future Operations Concepts of NASA's Earth Science Data and Information System

NASA committed to support the collection and distribution of Earth science data to study global change in the 1990's. A series of Earth science remote sensing satellites, the Earth Observing System (EOS), was to be the centerpiece. The concept for the science data system, the EOS Data and Information System (EOSDIS), created new challenges in the data processing of multiple satellite instrument observations for climate research and in the distribution of global-coverage remote sensor products to a large and growing science research community. EOSDIS was conceived to facilitate easy access to EOS science data for a wide heterogeneous national and international community of users. EOSDIS was to provide a spectrum of services designed for research scientists working on NASA focus areas but open to the general public and international science community. EOSDIS would give researchers tools and assistance in searching, selecting and acquiring data, allowing them to focus on Earth science climate research rather than complex product generation. Goals were to promote exchange of data and research results and expedite development of new geophysical algorithms. The system architecture had to accommodate a diversity of data types, data acquisition and product generation operations, data access requirements and different centers of science discipline expertise. Steps were taken early to make EOSDIS flexible by distributing responsibility for basic services. Many of the system operations concept decisions made in the 90s continued to this day. Once implemented, concepts such as the EOSDIS data model played a critical role developing effective data services, now a hallmark of EOSDIS. In other cases, EOSDIS architecture has evolved to enable more efficient operations, taking advantage of new technology and thereby shifting more resources on data services and less on operating and maintaining infrastructure. In looking to the future, EOSDIS may be able to take advantage of commercial compute environments for infrastructure and further enable large scale climate research. In this presentation, we will discuss key EOSDIS operations concepts from the 1990's, how they were implemented and evolved in the architecture, and look at concepts and architectural challenges for EOSDIS operations utilizing commercial cloud services

USE OF ARTIFICIAL FIDUCIAL MARKERS FOR USV SWARM COORDINATION

Typical swarm algorithms (leader-follower, artificial potentials, etc.) rely on knowledge about the pose of each vehicle and inter-vehicle proximity. This information is often obtained via Global Positioning System (GPS) and communicated via radio-frequency means.. This research examines the capabilities and limitations of using a fiducial marker system in conjunction with an artificial potential field algorithm to achieve inter-vehicle localization and coordinate the motion of unmanned surface vessels operating together in an environment where satellite and radio communications are inhibited. Using Gazebo, a physics-based robotic simulation environment, a virtual model is developed for incorporating fiducial markers on a group of autonomous surface vessels. A control framework using MATLAB and the Robot Operating System (ROS) is developed that integrates image processing, AprilTag fiducial marker detection, and artificial potential control algorithms. This architecture receives multiple video streams, detects AprilTags, and extracts pose information to control the forward motion and inter-vehicle spacing in a swarm of autonomous surface vessels. This control architecture is tested for a variety of trajectories and tuned so that the swarm can successfully maintain formation control.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Implementation and evaluation of the sensornet protocol for Contiki

Sensornet Protocol (SP) is a link abstraction layer between the network layer and the link layer for sensor networks. SP was proposed as the core of a future-oriented sensor node architecture that allows flexible and optimized combination between multiple coexisting protocols. This thesis implements the SP sensornet protocol on the Contiki operating system in order to: evaluate the effectiveness of the original SP services; explore further requirements and implementation trade-offs uncovered by the original proposal. We analyze the original SP design and the TinyOS implementation of SP to design the Contiki port. We implement the data sending and receiving part of SP using Contiki processes, and the neighbor management part as a group of global routines. The evaluation consists of a single-hop traffic throughput test and a multihop convergecast test. Both tests are conducted using both simulation and experimentation. We conclude from the evaluation results that SP's link-level abstraction effectively improves modularity in protocol construction without sacrificing performance, and our SP implementation on Contiki lays a good foundation for future protocol innovations in wireless sensor networks

Umbilical Cord: A system for ubiquitous computing

Ubiquitous computing aims to make computing widely available, easy to use, and completely transparent to the user. Umbilical Cord is intended to be the first step in researching ubiquitous computing at CSUSB. It implements a model scalable network with a client-server architecture that features consistent user interaction and global access to user data. It is based on the Linux operating system which can be leveraged for future research due to the open nature of its source code. It also features a scalable network swap

Gesture Disambiguation in Operating Systems

A visual operating system features multiple simultaneous clients displayed over a system user interface (UI). Examples of simultaneous clients are the accessibility client, the system UI, browsers, map-applications, etc. A touch gesture made by the user can be intended to be directed towards any one of the clients. However, in an architecture where no single component or client has global knowledge or ownership of all gestures, the client towards which the gesture should be directed is sometimes unclear. This disclosure describes techniques to disambiguate user gestures which can be incorporated in an operating system. The techniques enable simultaneously-running clients to define and claim gestures; provide a centralized mechanism to deny gestures; provide an expression of priority amongst competing clients; provide a mechanism for a parent client to defer a gesture to a child client; etc. Simultaneous access of a gesture by competing clients eliminates latency across the full breadth of client priorities

Extended adaptive neuro-fuzzy inference systems

This thesis presents a novel extension to the Adaptive Neuro-Fuzzy Inference System (ANFIS) which we call extended ANFIS (EANFIS). The extension includes the introduction of an output class based membership function architecture, in which each output class in a discrete output situation has its own membership function and in the case of a continuous output, only one class; the possibility of determining the structure of the rule base from the underlying structure of the input variables; the determination of a possibly non-symmetric membership function the parameters of which can be determined automatically from the given input variables; the possibility of incorporating global information on the input variables through a Linear Discriminant Analysis in combination with the local input variable structure as represented by the membership functions. The possibility of determining the structure of the rule section before the training process commences means that the proposed EANFIS architecture can be applied to possibility large scale practical problems, as it does not require the formation of all possible combination of rules before the training process commences. In other words, the EANFIS architecture together with its structure determining procedures overcomes the current limitation facing ANFIS architecture when applied to systems with large number of inputs. The possibility of determining a membership function from the input variables means the user no longer needs to select a membership function from a set of candidate membership functions. The possibility of incorporating global information on the input variables in addition to the local information on input variables means that the EANFIS architecture can take advantage when such global information might be useful in improving the performance of the Neuro-Fuzzy System. The new EANFIS architecture is evaluated on a number of standard benchmark problems, and have been found to have superior performance. In addition, as this is an EANFIS, rules can be extracted from the trained system, thus providing information on the way in which the underlying system is operating. The proposed EANFIS recommends itself readily for applications in practical systems